Thermal Image based Non Invasive Disease Diagnosis using Nature Inspired Algorithms

Thermal Image based Non Invasive Disease Diagnosis using Nature Inspired Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Thermal Image based Non Invasive Disease Diagnosis using Nature Inspired Algorithms Aditya Kataria, Ritu Tiwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7169643/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Passive infrared thermography offers a non-invasive, radiation-free modality for early detection of pathological conditions by capturing subtle surface-temperature anomalies. We propose a unified diagnostic framework that integrates automated ROI segmentation with validation, generative and geometric data augmentation to address class imbalance, deep-feature extraction, and metaheuristic feature selection to screen for breast cancer, diabetic foot ulcers, and thyroid nodules from thermal images. Generative data augmentation techniques systematically enlarge limited thermal imaging datasets to enhance model generalizability and reduce limitations in datasets. Techniques in ROI segmentation enable accurate detection of thermal differences between various disease categories. Deep learning neural models help in decoding intricate thermal patterns, which in turn transforms passive thermography from being an auxiliary diagnostic tool into a potential preliminary screening tool. On five public datasets - two of breast cancer, two of diabetic foot ulcers, and one of thyroid nodules; our method achieves 98.5%, 99.2%, and 97.85% accuracy, respectively, representing 3–5% absolute gains over unoptimized baselines. The present work brings together metaheuristic nature-inspired algorithms and computational intelligence to formulate a robust diagnostic platform adaptable for use in different domains of pathology, demonstrating considerable potential for non-invasive medical screening and real-time early detection strategies. Health sciences/Biomarkers/Diagnostic markers Health sciences/Diseases/Cancer/Breast cancer Health sciences/Diseases/Endocrine system and metabolic diseases/Thyroid diseases Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes Thermography Medical Diagnosis Deep Learning Nature Inspired Algorithms Non Invasive Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7169643","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":512772845,"identity":"ff5c472b-e239-49e2-adc7-e84c97312177","order_by":0,"name":"Aditya Kataria","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0003-2770-573X","institution":"Sardar Vallabhbhai National Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Kataria","suffix":""},{"id":512772846,"identity":"6d9c6bb7-aa28-440b-8af8-43746ea28082","order_by":1,"name":"Ritu Tiwari","email":"","orcid":"","institution":"Sardar Vallabhbhai National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ritu","middleName":"","lastName":"Tiwari","suffix":""}],"badges":[],"createdAt":"2025-07-20 12:35:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7169643/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7169643/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93697556,"identity":"0b9d84e0-d4ce-4d2e-99c2-9e91355816b8","added_by":"auto","created_at":"2025-10-16 14:58:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1999709,"visible":true,"origin":"","legend":"Article File","description":"","filename":"AdityaKatariaSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7169643/v1_covered_95bcb33c-a3fa-4293-b23c-61531042da8a.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Thermal Image based Non Invasive Disease Diagnosis using Nature Inspired Algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thermography, Medical Diagnosis, Deep Learning, Nature Inspired Algorithms, Non Invasive","lastPublishedDoi":"10.21203/rs.3.rs-7169643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7169643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Passive infrared thermography offers a non-invasive, radiation-free modality for early detection of pathological conditions by capturing subtle surface-temperature anomalies. 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